Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition

This work presents a pattern recognition system that is able to detect ambiguous patterns and explain its answers. The system consists of a set of parallel Support Vector Machine (SVM) classifiers, each one dedicated to a representative feature extracted from the input, followed by an analysing modu...

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Detalles Bibliográficos
Autores principales:	Seijas, Leticia María, Segura, Enrique Carlos
Publicado:	2009
Materias:	Ambiguous pattern Answer explanation Bayesian statistics Pattern recognition Support vector machine Bayesian Handwritten numeral Handwritten numeral recognition Module-based Bayesian networks Image analysis Image retrieval Support vector machines Pattern recognition systems
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v5702LNCS_n_p840_Seijas http://hdl.handle.net/20.500.12110/paper_03029743_v5702LNCS_n_p840_Seijas
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_03029743_v5702LNCS_n_p840_Seijas
record_format	dspace
spelling	paper:paper_03029743_v5702LNCS_n_p840_Seijas2023-06-08T15:28:32Z Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition Seijas, Leticia María Segura, Enrique Carlos Ambiguous pattern Answer explanation Bayesian statistics Pattern recognition Support vector machine Ambiguous pattern Answer explanation Bayesian Bayesian statistics Handwritten numeral Handwritten numeral recognition Module-based Bayesian networks Image analysis Image retrieval Support vector machines Pattern recognition systems This work presents a pattern recognition system that is able to detect ambiguous patterns and explain its answers. The system consists of a set of parallel Support Vector Machine (SVM) classifiers, each one dedicated to a representative feature extracted from the input, followed by an analysing module based on a bayesian strategy in charge of defining the system answer. We apply the system to the recognition of handwritten numerals. Experiments were carried out on the MNIST database, which is generally accepted as one of the standards in most of the literature in the field. © 2009 Springer Berlin Heidelberg. Fil:Seijas, L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Segura, E. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v5702LNCS_n_p840_Seijas http://hdl.handle.net/20.500.12110/paper_03029743_v5702LNCS_n_p840_Seijas
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Ambiguous pattern Answer explanation Bayesian statistics Pattern recognition Support vector machine Ambiguous pattern Answer explanation Bayesian Bayesian statistics Handwritten numeral Handwritten numeral recognition Module-based Bayesian networks Image analysis Image retrieval Support vector machines Pattern recognition systems
spellingShingle	Ambiguous pattern Answer explanation Bayesian statistics Pattern recognition Support vector machine Ambiguous pattern Answer explanation Bayesian Bayesian statistics Handwritten numeral Handwritten numeral recognition Module-based Bayesian networks Image analysis Image retrieval Support vector machines Pattern recognition systems Seijas, Leticia María Segura, Enrique Carlos Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition
topic_facet	Ambiguous pattern Answer explanation Bayesian statistics Pattern recognition Support vector machine Ambiguous pattern Answer explanation Bayesian Bayesian statistics Handwritten numeral Handwritten numeral recognition Module-based Bayesian networks Image analysis Image retrieval Support vector machines Pattern recognition systems
description	This work presents a pattern recognition system that is able to detect ambiguous patterns and explain its answers. The system consists of a set of parallel Support Vector Machine (SVM) classifiers, each one dedicated to a representative feature extracted from the input, followed by an analysing module based on a bayesian strategy in charge of defining the system answer. We apply the system to the recognition of handwritten numerals. Experiments were carried out on the MNIST database, which is generally accepted as one of the standards in most of the literature in the field. © 2009 Springer Berlin Heidelberg.
author	Seijas, Leticia María Segura, Enrique Carlos
author_facet	Seijas, Leticia María Segura, Enrique Carlos
author_sort	Seijas, Leticia María
title	Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition
title_short	Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition
title_full	Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition
title_fullStr	Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition
title_full_unstemmed	Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition
title_sort	detection of ambiguous patterns using svms: application to handwritten numeral recognition
publishDate	2009
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v5702LNCS_n_p840_Seijas http://hdl.handle.net/20.500.12110/paper_03029743_v5702LNCS_n_p840_Seijas
work_keys_str_mv	AT seijasleticiamaria detectionofambiguouspatternsusingsvmsapplicationtohandwrittennumeralrecognition AT seguraenriquecarlos detectionofambiguouspatternsusingsvmsapplicationtohandwrittennumeralrecognition
_version_	1768545646952841216

Detection of ambiguous patterns using SVMs: Application to handwritten numeral recognition

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